In [ ]:
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 27 08:34:49 2020
https://scvelo.readthedocs.io/
@author: Administrator
"""
import scvelo as scv
import scanpy as sc
import numpy as np
import matplotlib
# matplotlib.rcParams['image.cmap'] = 'RdBu_r' #default
scv.logging.print_version()
scv.settings.verbosity = 3  # show errors(0), warnings(1), info(2), hints(3)
scv.settings.set_figure_params('scvelo')  # for beautified visualization
scv.settings.set_figure_params('scvelo')

# in R
colorlist = ["#66C2A5", "#FC8D62", "#8DA0CB","#E78AC3","#A6D854", "#FFD92F", "#E5C494", "#FFFFB3", "#BEBADA", "#FB8072","#80B1D3" ,"#FDB462", "#BC80BD","#ff2121"]
# ['Endothelial1', 'Fibroblast1', 'SMC1', 'SMC2', 'Endothelial2',
#        'Fibroblast2', 'Myeloid1', 'T cell', 'Neuron', '9']

# in python
# ['9', 'Endothelial1', 'Endothelial2', 'Fibroblast1', 'Fibroblast2',
       # 'Myeloid1', 'Neuron', 'SMC1', 'SMC2', 'T cell']
# colorlist = ["#BC80BD", "#66C2A5", "#A6D854","#FC8D62", "#FFD92F", "#E5C494", "#BEBADA","#8DA0CB","#E78AC3", "#FFFFB3"]

matplotlib.colors.ListedColormap(colorlist, name='colorlist')
# matplotlib.rcParams["axes.prop_cycle"] = colorlist

Running scvelo 0.2.4 (python 3.9.7) on 2022-04-18 20:54.

ERROR: XMLRPC request failed [code: -32500]
RuntimeError: PyPI's XMLRPC API is currently disabled due to unmanageable load and will be deprecated in the near future. See https://status.python.org/ for more information.
Out[ ]:
colorlist
colorlist colormap
under
bad
over

从seurat载入anndata对象¶

In [ ]:
adata=sc.read('./PA_merge_velo.h5ad')
In [ ]:
# 不能这么写
# adata.obs.Classification1.values.categories = ['Endothelial1',  'Fibroblast1', 'SMC1', 'SMC2','Endothelial2', 'Fibroblast2',
#        'Myeloid1', 'T cell', 'Neuron','9'] #这个顺序与R结果对齐，从而保证配色相同
# adata.obs.Classification1.values.categories
Out[ ]:
Index(['Endothelial1', 'Fibroblast1', 'SMC1', 'SMC2', 'Endothelial2',
       'Fibroblast2', 'Myeloid1', 'T cell', 'Neuron', '9'],
      dtype='object')
In [ ]:
scv.pl.proportions(adata, groupby='Classification1')
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scv.pp.filter_and_normalize(adata, n_top_genes=4000)
sc.pl.umap(adata, color=['Classification1'], size=40, palette = colorlist)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)
scv.tl.velocity(adata)

Normalized count data: X, spliced, unspliced.
Extracted 4000 highly variable genes.
Logarithmized X.

computing neighbors
    finished (0:00:11) --> added 
    'distances' and 'connectivities', weighted adjacency matrices (adata.obsp)
computing moments based on connectivities
    finished (0:00:02) --> added 
    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)
computing velocities
    finished (0:00:14) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
In [ ]:
scv.tl.velocity_graph(adata)

computing velocity graph (using 1/8 cores)

    finished (0:02:52) --> added 
    'velocity_graph', sparse matrix with cosine correlations (adata.uns)
In [ ]:
scv.pl.velocity_embedding_stream(adata, basis='umap', color=['initial_size_spliced'], legend_loc='right margin', size=40, show = False)
scv.pl.velocity_embedding_stream(adata, basis='umap', color=['Classification1'], legend_loc='right margin', size=40, show = False)

computing velocity embedding
    finished (0:00:08) --> added
    'velocity_umap', embedded velocity vectors (adata.obsm)
Out[ ]:
<AxesSubplot:title={'center':'Classification1'}>

markers¶

静脉

In [ ]:
var_names = ["LUM", "ACTA2", "CNN1", "TNFRSF11B"]
scv.pl.velocity(adata, var_names=var_names, colorbar=True, ncols=2, color=['Classification1'], palette = colorlist, dpi=300)

动脉

In [ ]:
var_names = ["ACKR1", "EFNB2", "NOTCH4", "DLL4"]
scv.pl.velocity(adata, var_names=var_names, colorbar=True, ncols=2, color=['Classification1'], palette = colorlist, dpi=300)
In [ ]:
scv.pl.scatter(adata,color=['Classification1'], basis=["LUM", "ACTA2", "CNN1", "TNFRSF11B"], ncols=4, frameon=True, palette = colorlist, size=70)
In [ ]:
scv.tl.score_genes_cell_cycle(adata) 
scv.pl.scatter(adata, color_gradients=['S_score', 'G2M_score'], smooth=True, perc=[5, 95])

calculating cell cycle phase
-->     'S_score' and 'G2M_score', scores of cell cycle phases (adata.obs)
In [ ]:
scv.tl.velocity_confidence(adata)
scv.tl.velocity_pseudotime(adata)

scv.pl.scatter(adata, color='velocity_confidence', perc=[2,98])
scv.pl.scatter(adata, color='velocity_length')
scv.pl.scatter(adata, color='velocity_pseudotime', cmap='gnuplot')

--> added 'velocity_length' (adata.obs)
--> added 'velocity_confidence' (adata.obs)
--> added 'velocity_confidence_transition' (adata.obs)
computing terminal states
    identified 6 regions of root cells and 5 regions of end points .
    finished (0:00:12) --> added
    'root_cells', root cells of Markov diffusion process (adata.obs)
    'end_points', end points of Markov diffusion process (adata.obs)
In [ ]:
scv.tl.recover_dynamics(adata, n_jobs=6)
scv.tl.velocity(adata, mode='dynamical')
scv.tl.velocity_graph(adata, n_jobs=6)

recovering dynamics (using 6/8 cores)

    finished (0:19:45) --> added 
    'fit_pars', fitted parameters for splicing dynamics (adata.var)
computing velocities
    finished (0:01:08) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
computing velocity graph (using 6/8 cores)

    finished (0:00:43) --> added 
    'velocity_graph', sparse matrix with cosine correlations (adata.uns)
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scv.tl.latent_time(adata)
scv.tl.rank_velocity_genes(adata, match_with='Classification1', resolution=.4)

scv.pl.scatter(adata, color='latent_time', fontsize=24, size=100,
               color_map='gnuplot', perc=[2, 98], colorbar=True, rescale_color=[0,1])

computing latent time using root_cells as prior
    finished (0:00:16) --> added 
    'latent_time', shared time (adata.obs)
computing velocity clusters
    finished (0:00:04) --> added 
    'velocity_clusters', clusters based on louvain modularity on velocity vector field (adata.obs)
ranking velocity genes
    finished (0:00:24) --> added 
    'rank_velocity_genes', sorted scores by group ids (adata.uns) 
    'spearmans_score', spearmans correlation scores (adata.var)
In [ ]:
top_genes = np.unique(adata.var_names[adata.var.fit_likelihood.argsort()[::-1]][:300])
scv.pl.heatmap(adata, var_names=top_genes, tkey='latent_time', n_convolve=100, col_color='Classification1')
top_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index[:300]
scv.pl.heatmap(adata, var_names=top_genes, sortby='latent_time', col_color='Classification1', n_convolve=100)
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scv.pl.scatter(adata, basis=top_genes[:10], legend_loc='none',
               size=80, frameon=False, ncols=5, fontsize=20,color='Classification1')
scv.pl.scatter(adata, x='latent_time', y=top_genes[:4], fontsize=16, size=100,
               n_convolve=None, frameon=False, legend_loc='none',color='Classification1')
top_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index
scv.pl.scatter(adata, basis=top_genes[:15], ncols=5, frameon=False, color='Classification1')
In [ ]:
var_names = ["LUM", "ACTA2", "CNN1", "TNFRSF11B"]
scv.tl.differential_kinetic_test(adata, var_names=var_names, groupby='Classification1')
scv.get_df(adata[:, var_names], ['fit_diff_kinetics', 'fit_pval_kinetics'], precision=2)

testing for differential kinetics
    finished (0:00:03) --> added 
    'fit_diff_kinetics', clusters displaying differential kinetics (adata.var)
    'fit_pvals_kinetics', p-values of differential kinetics (adata.var)

outputs model fit of gene: TNFRSF11B
Out[ ]:
fit_diff_kinetics fit_pval_kinetics
TNFRSF11B SMC1 1.87e-07